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AI strengthens these capabilities by enhancing sensing through real-time analytics,
supporting seizing via predictive modeling, and facilitating transformation through
organizational learning (Teece, 2018). Absorptive capacity further mediates this process,
as firms require prior knowledge structures to recognize and assimilate external
information (Cohen & Levinthal, 1990). For export-oriented enterprises in global value
chains, exposure to international standards enhances learning opportunities, but effective
upgrading depends on internal innovation capability (Gereffi et al., 2005).
2.3. Digital transformation and export upgrading
Digital transformation involves structural changes in business models,
organizational processes, and value creation (Vial, 2019). Within global value chains,
upgrading refers to moving toward higher value-added activities such as design, branding,
and technological innovation (Gereffi et al., 2005; Sturgeon, 2021).
AI facilitates export upgrading by improving product customization, quality control,
logistics optimization, and compliance with international standards (OECD, 2023), thereby
enhancing both price and non-price competitiveness. However, digital transformation
remains uneven, particularly among small and medium-sized enterprises facing
constraints in financial resources, digital skills, and infrastructure (World Bank, 2022).
Without coordinated institutional support, AI adoption may widen competitiveness gaps
in emerging economies (UNCTAD, 2021).
2.4. Institutional environment and policy alignment
National innovation systems theory emphasizes the role of institutional structures
in shaping technological development and economic performance (Lundvall, 1992).
Governments coordinate research investment, regulatory frameworks, and skill
development to support innovation ecosystems.
The concept of the entrepreneurial state highlights the role of public investment
and policy coordination in driving technological advancement (Mazzucato, 2013). In the AI
domain, policy coherence is essential for data governance, cybersecurity, and digital trade
integration (OECD, 2023). For export-oriented firms, institutional alignment determines
access to infrastructure, financial incentives, and international markets, while fragmented
policies may weaken AI-driven transformation.
2.5. Research gap
While existing studies examine AI adoption, innovation capability, and export
competitiveness, integrated frameworks linking these elements remain limited. Few
studies connect AI adoption to export upgrading through innovation capability while
considering the moderating role of institutional alignment in emerging economies.
This study addresses this gap by proposing a conceptual and policy-oriented
framework linking technological adoption, firm capability development, and export
competitiveness within a national institutional context. As digital technologies reshape
global production networks, export competitiveness increasingly depends on firms’ ability
to integrate AI and data analytics to enhance productivity and innovation (Brynjolfsson &
McAfee, 2014).
AI enables firms to process large datasets, automate operations, and generate
predictive insights, thereby improving innovation performance and competitive
advantage (Cockburn et al., 2019). For export-oriented enterprises, these capabilities
enhance participation in global value chains through improved supply chain coordination
and market responsiveness. However, adoption remains uneven due to constraints in
infrastructure, skills, and financial resources (World Bank, 2021).

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